Online Prediction Modeling Tool to Help Advance Scientific Discoveries

By anticipating the location of proteins within a cell, researchers might have access to a trove of biological knowledge. Future scientific discoveries related to drug development and the treatment of diseases like epilepsy will depend on this information. This is due to the fact that proteins act as the body’s “workhorses,” being mostly in charge of most cellular processes.

Dong Xu, Curators Distinguished Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri, and associates recently improved the accuracy of their protein localization prediction model, MULocDeep, to include models, especially for plants, animals, and humans.

The model was created 10 years ago by Xu and Jay Thelen, a professor of biochemistry at MU, with the initial goal of analyzing proteins in mitochondria.

Many biological discoveries need to be validated by experiments, but we don’t want researchers to have to spend time and money conducting thousands of experiments to get there. A more targeted approach saves time. Our tool provides a useful resource for researchers by helping them get to their discoveries faster because we can help them design more targeted experiments from which to advance their research more effectively.”

Dong Xu, Curators Distinguished Professor, Department of Electrical Engineering and Computer Science, University of Missouri

By utilizing the power of artificial intelligence through a machine learning approach, the model can help researchers who are investigating the causes driving irregular protein placements, also known as “mislocalization,” or when a protein gets to a different area than it is meant to.

This aberration has a history of association with several diseases, including cancer, neurological disorders, and metabolic disorders.

Xu stated, “Some diseases are caused by mislocalization, which causes the protein to be unable to perform a function as expected because it either cannot go to a target or goes there inefficiently.”

By concentrating on and moving misplaced proteins, the team’s prediction model can also be utilized to aid in drug development, according to Xu.

The National Science Foundation is currently supporting the project. In the future, Xu hopes to make more money to help with accuracy improvements and feature additions.

“We want to continue improving the model to determine whether a mutation in a protein could cause mislocalization, whether proteins are distributed in more than one cellular compartment, or how signal peptides can help predict localization more precisely. While we don’t offer any solutions for drug development or treatments for various diseases per se, our tool may help others for their development of medical solutions. Today’s science is like a big enterprise. Different people play different roles, and by working together we can achieve a lot of good for all,” Xu further stated.

The course will be based on the biological and bioinformatics concepts used in the model, and Xu is also working with colleagues to provide a free, online course for high school and college students. He expects the course to be available later this year.

MULocDeep offers an online version that academic users may use, but a standalone version is also made available commercially in exchange for a license fee, as pointed out by Xu and colleagues.